{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import signal,interpolate\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "import wfdb\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False #用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义求RR间期的函数\n",
    "def RR(signal,fs=250):\n",
    "    rr = [] #用来存储RR间期的数组\n",
    "    R_pot = [] #用来存储满足条件R点的数组\n",
    "    for index,value in enumerate(signal):\n",
    "        if(index>0):\n",
    "            rr_pot = (value-signal[index-1])/fs\n",
    "            #去除异常的RR间期数值，保留[0.3,1.5]区间的数值\n",
    "            if(rr_pot < 1.5 and rr_pot >0.3):\n",
    "                rr.append(rr_pot)\n",
    "                R_pot.append(value)\n",
    "    return rr,R_pot\n",
    "# 线性内插函数\n",
    "# def Interplt(signal):\n",
    "#     new_signal = [signal[0]] #用来存储插值后的信号\n",
    "#     for index,value in enumerate(signal):\n",
    "#         if(index>0):\n",
    "#             new_pot = (value+signal[index-1])/2\n",
    "#             new_signal.extend([new_pot,value])\n",
    "#     return new_signal\n",
    "def Interplt(rr, R_pot,fs=250):            \n",
    "    x = np.array(R_pot)/fs\n",
    "    y = rr\n",
    "    tck = interpolate.splrep(x, y, s=0)\n",
    "    t = 8*60\n",
    "    xnew = np.arange(0,t,0.5)\n",
    "    ynew = interpolate.splev(xnew, tck, der=0)\n",
    "    return xnew,ynew\n",
    "# 快速傅里叶变换FFT\n",
    "def FFT(signal,Fs):\n",
    "\n",
    "    axis_t=range(len(signal))\n",
    "    y=signal\n",
    "\n",
    "    n = len(y)                  # length of the signal\n",
    "    k = np.arange(n)\n",
    "    T = n/Fs\n",
    "    frq = k/T                   # two sides frequency range\n",
    "    frq1 = frq[range(int(n/2))] # one side frequency range\n",
    "\n",
    "    YY = np.fft.fft(y)          # 未归一化\n",
    "    Y = np.fft.fft(y)/n         # fft computing and normalization 归一化\n",
    "    Y1 = Y[range(int(n/2))]\n",
    "\n",
    "    fig, ax = plt.subplots(4, 1)\n",
    "\n",
    "    ax[0].plot(axis_t,y)\n",
    "    ax[0].set_xlabel('Time')\n",
    "    ax[0].set_ylabel('Amplitude')\n",
    "\n",
    "    YY[0] = 0\n",
    "    Y[0] = 0\n",
    "    Y1[0] = 0\n",
    "\n",
    "    ax[1].plot(frq, abs(YY),'r') # plotting the spectrum\n",
    "    ax[1].set_xlabel('Freq (Hz)')\n",
    "    ax[1].set_ylabel('|Y(freq)|')\n",
    "\n",
    "    ax[2].plot(frq, abs(Y),'G')  # plotting the spectrum\n",
    "    ax[2].set_xlabel('Freq (Hz)')\n",
    "    ax[2].set_ylabel('|Y(freq)|')\n",
    "\n",
    "    ax[3].plot(frq1, abs(Y1),'B') # plotting the spectrum\n",
    "    ax[3].set_xlabel('Freq (Hz)')\n",
    "    ax[3].set_ylabel('|Y(freq)|')\n",
    "    return frq1\n",
    "\n",
    "# butter带通滤波\n",
    "def butter_bandpass(data, fre, lowcut, highcut, order=3):\n",
    "    nyq = 0.5 * fre\n",
    "    low = lowcut / nyq\n",
    "    high = highcut / nyq\n",
    "    b, a = signal.butter(order, [low, high], btype='band')\n",
    "    new_sig = signal.filtfilt(b, a, data)\n",
    "    return new_sig\n",
    "# butter高通滤波\n",
    "def butter_highpass(data, lowcut, fre, order=3):\n",
    "    nyq = 0.5 * fre\n",
    "    low = lowcut / nyq\n",
    "    b, a = signal.butter(order, low, btype=\"high\")\n",
    "    new_sig = signal.filtfilt(b, a, data)\n",
    "    return new_sig\n",
    "# butter低通滤波\n",
    "def butter_lowpass(data, highcut, fre, order=3):\n",
    "    nyq = 0.5 * fre\n",
    "    high = highcut / nyq\n",
    "    b, a = signal.butter(order, high, btype=\"low\")\n",
    "    new_sig = signal.filtfilt(b, a, data)\n",
    "    return new_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class HMM:\n",
    "    def __init__(self,A,B,pi):\n",
    "        self.A =A\n",
    "        self.B =B\n",
    "        self.pi =pi\n",
    "\n",
    "    # 前向算法\n",
    "    def _forward(self,obs_seq):\n",
    "            # 取A = N x N\n",
    "            N = self.A.shape[0]\n",
    "            T = len(obs_seq)\n",
    "\n",
    "            F = np.zeros((N,T))\n",
    "\n",
    "            # alpha = pi*b\n",
    "            F[:,0] = self.pi *self.B[:,obs_seq[0]]\n",
    "\n",
    "            for t in range(1,T):\n",
    "                for n in range(N):\n",
    "                    # 计算第t时，第n个状态的前向概率\n",
    "                    F[n,t] = np.dot(F[:,t-1], (self.A[:,n])) * self.B[n, obs_seq[t]]\n",
    "            return F\n",
    "# 调用：\n",
    "# h = HMM(A,B,pi)\n",
    "# # 人为定义的海藻状态序列\n",
    "# obs_seq = ('dry','damp','soggy')\n",
    "# obs_seq_index = convert_observations_to_index(obs_seq,observations_label_index)\n",
    "\n",
    "# # 计算P(o|lambda)\n",
    "# F = h._forward(obs_seq_index)\n",
    "# print (\"forward: P(O|lambda) = %f\" %sum(F[:,-1]))\n",
    "\n",
    "    # 后向算法\n",
    "    def _backward(self,obs_seq):\n",
    "            N = self.A.shape[0]\n",
    "            T = len(obs_seq)\n",
    "\n",
    "            X = np.zeros((N,T))\n",
    "            # 表示X矩阵的最后一列\n",
    "            X[:,-1:] =1\n",
    "\n",
    "            for t in reversed(range(T-1)):\n",
    "                for n in range(N):\n",
    "                    # 边权值为a_ji\n",
    "                    X[n,t] = sum(X[:,t+1] * self.A[n,:] * self.B[:,obs_seq[t+1]])\n",
    "\n",
    "            return X\n",
    "        \n",
    "    #     baum_welch_train算法\n",
    "    def baum_welch_train(self, observations, criterion=0.05):\n",
    "        n_states = self.A.shape[0]\n",
    "        # 观察序列的长度T\n",
    "        n_samples = len(observations)\n",
    "\n",
    "        done = False\n",
    "        while not done:\n",
    "            # alpha_t(i) = P(o_1,o_2,...,o_t,q_t = s_i | hmm)\n",
    "            # Initialize alpha\n",
    "            # 获得所有前向传播节点值 alpha_t(i)\n",
    "            alpha = self._forward(observations)\n",
    "\n",
    "            # beta_t(i) = P(o_t+1,o_t+2,...,o_T | q_t = s_i , hmm)\n",
    "            # Initialize beta\n",
    "            # 获得所有后向传播节点值 beta_t(i)\n",
    "            beta = self._backward(observations)\n",
    "\n",
    "            # 计算 xi_t(i,j) -> xi(i,j,t)\n",
    "            xi = np.zeros((n_states, n_states, n_samples - 1))\n",
    "            # 在每个时刻\n",
    "            for t in range(n_samples - 1):\n",
    "                # 计算P(O | hmm)\n",
    "                denom = sum(alpha[:, -1])\n",
    "                for i in range(n_states):\n",
    "                    # numer[1,:] = 行向量，alpha[i,t]=实数，slef.A[i,:] = 行向量\n",
    "                    # self.B[:,observations[t+1]].T = 行向量,beta[:,t+1].T = 行向量\n",
    "                    numer = alpha[i, t] * self.A[i, :] * self.B[:, observations[t + 1]].T * beta[:, t + 1].T\n",
    "                    xi[i, :, t] = numer / denom\n",
    "\n",
    "                # 计算gamma_t(i) 就是对j进行求和\n",
    "                gamma = np.sum(xi, axis=1)\n",
    "                # need final gamma elements for new B\n",
    "                prod = (alpha[:, n_samples - 1] * beta[:, n_samples - 1]).reshape((-1, 1))\n",
    "                # 合并T时刻的节点\n",
    "                gamma = np.hstack((gamma, prod / np.sum(prod)))\n",
    "                # 列向量\n",
    "                newpi = gamma[:, 0]\n",
    "                newA = np.sum(xi, 2) / np.sum(gamma[:, :-1], axis=1).reshape((-1, 1))\n",
    "                newB = np.copy(self.B)\n",
    "\n",
    "                # 观测状态数\n",
    "                num_levels = self.B.shape[1]\n",
    "                sumgamma = np.sum(gamma, axis=1)\n",
    "                for lev in range(num_levels):\n",
    "                    mask = observations == lev\n",
    "                    newB[:, lev] = np.sum(gamma[:, mask], axis=1) / sumgamma\n",
    "\n",
    "                if np.max(abs(self.pi - newpi)) < criterion and \\\n",
    "                                np.max(abs(self.A - newA)) < criterion and \\\n",
    "                                np.max(abs(self.B - newB)) < criterion:\n",
    "                    done = 1\n",
    "                self.A[:], self.B[:], self.pi[:] = newA, newB, newpi\n",
    "        return A,B,pi\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将75种状态值转换为字典的键\n",
    "def gengerate_index_map(labels):\n",
    "    label_index = {}\n",
    "    i =0\n",
    "    for l in labels:\n",
    "        label_index[l] =i\n",
    "        i+=1\n",
    "    return label_index\n",
    "# 将随机观察值（键）转为观察值字典中的值（有序数组）\n",
    "def convert_observations_to_index(observations,label_index):\n",
    "    list =[]\n",
    "    for o in observations:\n",
    "        list.append(label_index[o])\n",
    "    return list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n#  Download all the WFDB records and annotations from Polysomnograghic Database(slpdb)（已下载完成，先将此模块注释掉）\\n\\n# Make a download directory in current working directory\\ncwd = os.getcwd()\\ndl_dir = os.path.join(cwd, 'slpdb_dl_dir')\\n\\n# Download all the WFDB content\\nwfdb.dl_database('slpdb', dl_dir=dl_dir)\\n\\n# Display the downloaded content in the folder\\n# display(os.listdir(dl_dir))\\n\\n# Cleanup: delete the downloaded directory\\n# shutil.rmtree(dl_dir)\\n\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "#  Download all the WFDB records and annotations from Polysomnograghic Database(slpdb)（已下载完成，先将此模块注释掉）\n",
    "\n",
    "# Make a download directory in current working directory\n",
    "cwd = os.getcwd()\n",
    "dl_dir = os.path.join(cwd, 'slpdb_dl_dir')\n",
    "\n",
    "# Download all the WFDB content\n",
    "wfdb.dl_database('slpdb', dl_dir=dl_dir)\n",
    "\n",
    "# Display the downloaded content in the folder\n",
    "# display(os.listdir(dl_dir))\n",
    "\n",
    "# Cleanup: delete the downloaded directory\n",
    "# shutil.rmtree(dl_dir)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y2SyynPM3d63Fn1ajIyCIM8zIo759PE05OoRDSyhxCoft2StU2zK4ee6ac8zr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\nUPt4rgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16feef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'adc_gain': [-200.0],\n",
       " 'adc_res': [12],\n",
       " 'adc_zero': [0],\n",
       " 'base_counter': '94',\n",
       " 'base_date': '19/1/1989',\n",
       " 'base_time': '23:07:00',\n",
       " 'baseline': [0],\n",
       " 'block_size': [0],\n",
       " 'byte_offset': [None],\n",
       " 'checksum': [36435],\n",
       " 'comments': ['44 M 89 32-01-89'],\n",
       " 'counter_freq': '0.033333333',\n",
       " 'd_signal': None,\n",
       " 'e_d_signal': None,\n",
       " 'e_p_signal': None,\n",
       " 'file_name': ['slp01a.dat'],\n",
       " 'fmt': ['212'],\n",
       " 'fs': 250,\n",
       " 'init_value': [-17],\n",
       " 'n_sig': 1,\n",
       " 'p_signal': array([[ 0.085],\n",
       "        [ 0.08 ],\n",
       "        [ 0.125],\n",
       "        ...,\n",
       "        [-0.   ],\n",
       "        [-0.   ],\n",
       "        [-0.   ]]),\n",
       " 'record_name': 'slp01a',\n",
       " 'samps_per_frame': [1],\n",
       " 'sig_len': 15000,\n",
       " 'sig_name': ['ECG'],\n",
       " 'skew': [None],\n",
       " 'units': ['mV']}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'ann_len': 3,\n",
       " 'aux_note': ['4 LA LA', '4 LA', '4 LA'],\n",
       " 'chan': array([0, 0, 0]),\n",
       " 'contained_labels': None,\n",
       " 'custom_labels': None,\n",
       " 'description': None,\n",
       " 'extension': 'st',\n",
       " 'fs': 250,\n",
       " 'label_store': None,\n",
       " 'num': array([0, 0, 0]),\n",
       " 'record_name': 'slp01a',\n",
       " 'sample': array([    1,  7500, 15000]),\n",
       " 'subtype': array([0, 0, 0]),\n",
       " 'symbol': ['\"', '\"', '\"']}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'============='"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The fs of slp01a:\n",
      " 250\n",
      "The annotition of R on ecg sample slp01a:\n",
      " [   19   234   457   687   911  1137  1360  1581  1808  2029  2244  2460\n",
      "  2673  2890  3117  3354  3599  3841  4080  4327  4576  4822  5061  5292\n",
      "  5526  5761  5998  6234  6460  6682  6906  7134  7360  7578  7787  7993\n",
      "  8202  8418  8643  8874  9104  9352  9606  9857 10106 10343 10577 10808\n",
      " 11038 11268 11490 11710 11933 12165 12407 12647 12873 13093 13306 13520\n",
      " 13736 13954 14176 14414 14664 14912]\n",
      "annotition of sleep station on ecg sample slp01a:\n",
      " [    1  7500 15000]\n"
     ]
    }
   ],
   "source": [
    "# Display sample information\n",
    "record = wfdb.rdrecord('slpdb_dl_dir/slp01a', channels=[0],sampto = 15000)\n",
    "ann_ecg = wfdb.rdann('slpdb_dl_dir/slp01a','ecg',sampto = 15000)\n",
    "ann_st = wfdb.rdann('slpdb_dl_dir/slp01a','st',sampto = 15000)\n",
    "\n",
    "wfdb.plot_wfdb(record=record, annotation=ann_ecg, title='ecg signals of slp01a')\n",
    "display(record.__dict__)\n",
    "# display(\"=============\")\n",
    "# display(ann_ecg.__dict__)\n",
    "# display(\"=============\")\n",
    "display(ann_st.__dict__)\n",
    "display(\"=============\")\n",
    "print(\"The fs of slp01a:\\n\",record.fs)\n",
    "print(\"The annotition of R on ecg sample slp01a:\\n\",ann_ecg.sample)\n",
    "print(\"annotition of sleep station on ecg sample slp01a:\\n\",ann_st.sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'RR intervals signal:Time(30s)')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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14ageo8xnA+ezS+NX81pREiMNpeujnjvtZwznEgCPMfMXRPRPABMBfGRzm6Ki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\nOOTez7FlbwvWbG8I++3ql6t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C0gIXDuzVOe7azkbcPnEo7jvrIJx4YKRp6ZSDpO96l0nJ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\nDgxmZtbGgcHMzNo4MJiZWRsHBjMza/MbunbpP/1RzskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1701a7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算RR间期\n",
    "record = wfdb.rdrecord('slpdb_dl_dir/slp01a', channels=[0])\n",
    "ann_ecg = wfdb.rdann('slpdb_dl_dir/slp01a','ecg')\n",
    "ann_st = wfdb.rdann('slpdb_dl_dir/slp01a','st')\n",
    "\n",
    "# print(ann_st.aux_note[:10])\n",
    "st = [] #用来存储睡眠状态标签的数组\n",
    "for aux_note in ann_st.aux_note:\n",
    "    st.append(aux_note.split(\" \")[0])\n",
    "\n",
    "rr,R_pot = RR(ann_ecg.sample)          \n",
    "#Display the RR intervals and sleep staged by PhisioNet\n",
    "t = record.sig_len/record.fs      #信号长度（s）  \n",
    "fig, ax = plt.subplots(2, 1)\n",
    "# 将MT（Moment time 运动状态替换为W）\n",
    "for index, value in enumerate(st):\n",
    "    if value=='MT':\n",
    "        st[index]='W'\n",
    "        \n",
    "axis_st = [t*value/(30*len(ann_st.sample)) for value in range(0,len(ann_st.sample))]\n",
    "ax[0].plot(axis_st,st)\n",
    "ax[0].set_ylabel('sleep station')\n",
    "ax[0].set_xlabel('页:Time(30s)')\n",
    "\n",
    "axis_rr = [t*value/(30*len(rr)) for value in range(0,len(rr))]\n",
    "ax[1].plot(axis_rr,rr)\n",
    "ax[1].set_ylabel('RIS(ms)')\n",
    "ax[1].set_xlabel(\"RR intervals signal:Time(30s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[64, 15, 12, 0, 213, 63]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ym/PpX+eY9c8VBjevyJAWFXGwzL3ux50/jwby03/XGdi0QpEiVcyNjfjy5dq8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\n3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17090e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 下面给出睡眠各期RR值时序图，以便观察不同睡眠分期RR间期的分布\n",
    "stations = ['1','2','3','4','R','W']\n",
    "stations_index = []\n",
    "# 分别找出6个30s睡眠分期的索引\n",
    "for station in stations:\n",
    "    for index,value in enumerate(st):\n",
    "        if(value == station):\n",
    "            stations_index.append(index)\n",
    "            break\n",
    "print(stations_index)\n",
    "\n",
    "for value in stations_index:\n",
    "    sig_start = value*record.fs*30\n",
    "    sig_end = (value+1)*record.fs*30\n",
    "    ecg_value_index = []\n",
    "    for index,value in enumerate(ann_ecg.sample):\n",
    "        if(value >= sig_start and value <= sig_end):\n",
    "            ecg_value_index.append(index)\n",
    "    ecg_value = ann_ecg.sample[ecg_value_index[0]:ecg_value_index[-1]]\n",
    "    plt.plot(RR(ecg_value)[0])\n",
    "plt.legend(['N1','N2','N3','N4','REM','Awake'], loc=\"upper right\")\n",
    "plt.title(\"睡眠各期RR值时序图\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RR间期最低频率： 0.0001388888888888889\n",
      "RR间期最高频率： 0.5418055555555555\n"
     ]
    },
    {
     "data": {
      "image/png": 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XRMRnZiYi14pIjojk5OXZeDSxVMvEVIB3dV0wVXcmttwlCitIeLv9jJoTV7l5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\nkA8sBoYDycAMYHaE4jLGGNNAhJtRlKj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17280be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# FFT变换，对RR间期进行频谱分析，可以看出信号频率介于0~0.6Hz 之间\n",
    "Fs = len(rr)/t             # sampling rate采样率为1（约等于）\n",
    "signal = rr\n",
    "fs_distribute = FFT(signal,Fs)\n",
    "print('RR间期最低频率：', min(fs_distribute[1:]))\n",
    "print('RR间期最高频率：',max(fs_distribute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 根据香农定理：采样频率不能低于信号最高频率的2倍，进行二次采样，采样频率2Hz\n",
    "# # RR 间期时间序列事实上是一个非均匀采样序列，国内学者一般认为这种非均匀性非常小，可近似的认为该序列在时间轴上是均匀采样的\n",
    "# # 原采样频率\n",
    "# fs_raw = len(rr)/t\n",
    "# print('RR间期原始采样频率：',fs_raw)\n",
    "# fs_re = 2*fs_raw\n",
    "# # 重采样方法：线性内插法进行插值\n",
    "# rr_new = Interplt(rr)\n",
    "\n",
    "# axis_rr_new = [t*value/(30*len(rr_new)) for value in range(0,len(rr_new))]\n",
    "# plt.plot(axis_rr_new,rr_new)\n",
    "# plt.title(\"RR intervals signal after re-sample:Time(30s)\")\n",
    "# plt.show()\n",
    "\n",
    "# # signal = rr_new\n",
    "# # fs_distribute = FFT(signal,2)\n",
    "# # print('RR间期最低频率：', min(fs_distribute[1:]))\n",
    "# # print('RR间期最高频率：',max(fs_distribute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z8s+XeWZ5Ki/cMJB5m7KoMSlRFofzyu3H3T4y1s2Xorqu1cZeb7XxfJh8kg+m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\nEOSeaTQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16dca0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 因为RR间期是一个非均匀采样序列，考虑用三次样条插值进行重采样，重采样频率为2HZ（参考庄志论文）\n",
    "from scipy import interpolate\n",
    "\n",
    "# x = np.arange(0, 2*np.pi+np.pi/4, 2*np.pi/8)\n",
    "# y = np.sin(x)\n",
    "x = np.array(R_pot)/record.fs\n",
    "y = rr\n",
    "tck = interpolate.splrep(x, y, s=0)\n",
    "# xnew = np.arange(0,2*np.pi,np.pi/50)\n",
    "xnew = np.arange(0,t,0.5)\n",
    "ynew = interpolate.splev(xnew, tck, der=0)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x, y, 'x', xnew, ynew, x, y, 'b')\n",
    "plt.legend(['Linear', 'Cubic Spline'])\n",
    "# plt.axis([-0.05, 6.33, -1.05, 1.05])\n",
    "plt.title('Cubic-spline interpolation')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将重采样的RR间期序列赋给rr_new\n",
    "rr_new = ynew\n",
    "axis_rr_new = xnew/30\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GUU/KH9cJe/zXLxajw98nYXxRqJ9739dm4s2pawKNr8LZTaU4TAM7nR5yPN7B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\nwd/c1wszLLhAZmWkhUgevtT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DSSCs9BnGR1zQLjvRIjAJhs07DMMwPoKVPsMwjI+ImdIn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\nK32GYRgf8f+NqxzE//qJTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17190b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RR间期最低频率： 0.0001388888888888889\n",
      "RR间期最高频率： 0.9998611111111111\n"
     ]
    },
    {
     "data": {
      "image/png": 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xJqeR1ibsG3xritH11HhkACNsL7AOg/o7cKsPNVVbbhvU1ivXDDUJYzxqD/ZK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\nEBYG8fGWUQD47DNo1Kjs/wkTJ5a8r1vX2n+xZo3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1690bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import signal\n",
    "# # 通过Butter 高通或带通滤波器，去除直流分量和基线漂移0~0.0001Hz\n",
    "# # ECG频率范围为0.05~100hz，RR间期频率在0~0.6Hz间,对比滤波效果，本次采用lowcut = 0.005 ,因为和基线漂移频率接近，易去掉有效低频部分\n",
    "# # 而HRV的超低频部分（0.0033~0.04Hz）和睡眠的关系比较密切，因此本项目不采用butter滤波器\n",
    "# lowcut1 = 0.005\n",
    "# lowcut2 = 0.01\n",
    "# highcut = 0.6\n",
    "# rr_filt1 = butter_highpass(rr_new, lowcut1, fs_re, order=3)\n",
    "# rr_filt2 = butter_highpass(rr_new, lowcut2, fs_re, order=3)\n",
    "# # rr_filt = butter_lowpass(rr_new, highcut, fs_re, order=3)\n",
    "# # rr_filt = butter_bandpass(rr_new, fs_re, lowcut, highcut, order=3)\n",
    "\n",
    "# 中值滤波较好的去除了直流分量和基线漂移，而为去除有效低频部分\n",
    "rr_filt = rr_new - signal.medfilt(rr_new,101) #中值滤波,第二个参数是邻域的大小(奇数)\n",
    "\n",
    "plt.plot(axis_rr_new,rr_filt)\n",
    "plt.title(\"RR intervals signal after filting: Time(30s)\")\n",
    "# plt.legend(['lowcut1 = 0.0005','lowcut2 = 0.01'], loc=\"upper right\")\n",
    "plt.show()\n",
    "\n",
    "signal = rr_filt\n",
    "fs_distribute = FFT(signal,2)\n",
    "print('RR间期最低频率：', min(fs_distribute[1:]))\n",
    "print('RR间期最高频率：',max(fs_distribute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ztJFZMJ5OUEnVg/diL5u70XgxlBOe+mEp+rSug4rlyxl6flgpjM9nbsBGl37i\nRumtUL0W9Oe2FRzGul2FtkPkEmZHqxbtCDf92DFnw17MXm99j5WuDY8rsveQu1g2RgtcJi1TerxO\nsDIv/aAzC5gttIsli7cUYJUaS+enxcHYYSoZsXKssPNcA5TRuL4+HTp2HFNX7sK57euHXJfoHnxS\nKfifl8a3Qmu73Jhh9fJemmg9k2+l4A3P6T5uA1+bFjJ550U+N+z1ECzsnhHezQUTloYqaLuAcfp8\nFhwuwi2fzo24ZnFegeOet1W56c0g2sKseHLBm/ZxdAR7mtbJMj3nxKz58DeL8NY1nUr//td3SzF6\n/haMvaeX7YYsXgMgeiGpTDS7DziLWGeHX0Mkv70WNCVuNKwrCVNiTkh078EKNx4t5W0ajD4lq6G3\nUwJcbIIHjEZkNSsbuzeH3md+bkt+6Oh8W4Gyucf+sNGnYRpxNNEklYL3y57ul+Kbvc7b5CwArN0V\nOXu/3yDsrob2MVniwv47x2ZBUbJg9zGI5n0a2ffNYpjY4SbOyq8rdmDjnkLTTWgmL3cWF0WwZvGW\nAmzJj9xZyazKzFi7u3THKstqRYSSEsaEpdsxa92eUs+6qSt32u4DLV40JgQtKtxDoxfh8i6NPN1r\nNTdgpLA0TxYzzwojguxV4W4LNOvzXhUyAMPQtUM+tw5FbYabEMFajBgzXplkv1hHsMfMpGVWp/Rh\nwq3mYhZu3oev5m4u3QFL4/1p63Fmm7qlzzBKIZ4jxLTswQcdv+LBpwp2OY9mQ5FNLtclCKlC9O1p\nm8k2lEd1o0LDdhvHppxUCj7o+t1oFygvLDMY6peUKDsupQp2XjZ6whf6hONlEriU9P1upjXHjrNt\nPKkFNns4L9picl6tUyt3HMAvBqNL2XTbhERG0TNjim6lo9EuUH5x4GgRbv3MelifTBjtEGTGQher\nVd0i+j09eWH8ctt6ZbdQbepKk53pdGrKqJ4n44YfccHLXpSxZmtB5AROLPB7JxxBIZ1NX+nMiu32\n+/LGimTYkzUhBFC/4/ExS+LynHhss5eO7D0Uv9jcQnBwtKeBR4Kkp5JKwafLJKsRfi3XF0L5fVX0\nG8ALgp4gmZKTSsEHqNzijtt4MIIgCEmm4NNXw386c0OiRRAEwQH7XMZOiiVJpeDT2Q69eW98JnMF\nQYgOu43XLz61geV5PxEFLwiCEEde+lvHuD0rqRR8EN0kBUEQ3KBtRRkPkkrBp7MNXhAEwS2+Kngi\n+pCIZhLRE36mKwiCILjHNwVPRIMBlGPmHgCaE1Erv9LWkFWHgiAIzvGzB98XwCj190QAvXxMGwBw\nekvZg1IQBMEpfir4LAB56u/b0zY4AAAcDElEQVS9AHL0J4loCBHNJaK5u3Z5Wz14S69m0UkoCIKQ\nRvip4A8CqKz+rhqeNjMPZ+ZcZs7Nzs729ACZZBUEQXCOnwp+HsrMMh0BbPAxbUEQBMElfsaD/w7A\nNCJqAGAQgO4+pi0IgiC4xLcePDPvhzLROgvAmcwcu10aBEEQBFt83dGJmfNR5kkjCIIgJJCkWskq\nCIIQZBrVrmx/URwRBS8IguATnRrXSrQIIYiCFwRB8InxS7YnWoQQRMELgiC44MMbcvHggNaG547F\ncK9XL4iCFwQhJbn9jOYxSbffyTm45yzfQ23FBFHwgiCkJOVk5XvyKfgRt3VLtAi+cJFP23Y9e1E7\nX9IRhFQjw0bBm5lZNP43xP1azYcGtgn5e8xdPTHz0bNK/9b/jgdJp+A7nFgz0SL4QrM6WYbHq2SW\nc5XO+R3it7+jICQTdjvAXdr5RMvzrXOquX5mnayKIX+f1rgW6tcoc53U/44HSafgU33Q5TbkfYXy\nSfcKBSEuZFeraHnerodfzsMeoUGzCiWddghaAXqFTD5VDMYT552MT27qgpd1m/N+dnNXw+urVvR1\nMTIA4JFBJ/mept/86/y2vqXlNAx1gxqVfHtmMvLL/X0SLYIpDw5ojWFXnorercr2jLiscyPT66c8\n2Nf03Gc3d8W9/VqhRuUKls+c/MAZEcfMPgoT7uuDH4f6vkWGLcmn4B304U/IyoyDJNHBMO6qlzBw\na+/m6NumbsgQsk9rbyGWndK3TVn6V3VpHHE+y6XpKNZc2NE/09QpDas7uu4Cn+ZNkpWWdd2bLOLF\nPWe1wkWnNsTnt5TN0WVYaDczEykAdG5SC/f3t7bPA0CL7Kpo1yC07pgp+Db1quGUhjVs0/Sb5FPw\nKdKDNyVBuxLqi7VcuVQv5FCKg+W6LLjELDyAnReNHzuANq5dJeRvO7NPvElbBf/tXT3x+lWn+ZOY\nB8xGIiUWtW7CfbEbImubqRApZp93rumE9roeR6w3W7nMZsIrHLf78559co7puZISh2kFdEvg1jlV\nHV/r1bT1/OD2AIAPrs91fW+Xpv4t3w/3Ght+XWd8c0fPkGMPDmiN1686DeXLZeDdazujYc3QD0CL\nbKX3bjaKtqrqbeuH9thfvKwDHju3zKTpxW4fS5JPwfs0zdqpcS1fh/kAcE47cyXiFCsd0qae9RC5\nU2N3HkaVKpS9/vBSHdS+fsgkVayr7Xnt67u6XtPJdW0m0jQa1jS3n1t9VFONgafUc33PhPv64Kqu\nitnu7Lbu6/jf+9mbO5zSqUnox2JAu3rIqR76bu85q1Vp2x54Sj3UrR5aR7TOitlrt9IxPVucEPJ3\ntUoVcGmnss5JwPR7Eip4BwWYqObqZnhm1nuw65meWCs2blZGogesroaglZ/TMrcagTjtwPtFy7rO\ne9xOcPN9SsQ7DZjVAnWqKnN0ZsVmJa/RPfq6JSaaKHFSfG6H706596yWlufLl3NfnA1rVsbXd/TA\nvy8+BYD9x+mDG9wPkY14oH9rZIbIa1Oy6ulnLz7Fca/Zb27o0aT0d3GJpuCjT7fYoL7ovTE0rN5N\n/RqVDE0l957VEpP+0QcfXJ+L96/PxTd39MA713TCi5d2cCSbk1EmA7jA4WjUSv+YmXrC75lwXx98\nfGMXR89zwtkn56BzE3szzs/39fY0gg+/462rO7lOQ8NItWTqXJX1Jppx98bfayac5FPwCfxC3j+g\njeX5Ch4mJ8/vUB9dmtYuHebZfZtOqufM48OOi05tGPK3YQ/e4FivlnXw4DnW5QA4s6mf2sidSenp\ni04p/a2Vk5f6cFKYqSu8Q1CveiVUr2TtImfE5bmRbnmXdj4RrXKq4ey2OejfNge5TWujVU41XN7F\n3IVPz7nt7U0qJcw416HpxaqHqTc1WNGmXjWceVJdR9c64YMbcnHT6U1tr/Or7p9Q1XsHxWjkrS9R\nrXw7NqqJdg3i7zUTTtIpeCc9tkSZaDI99OC12mHl0uUUI7/d+ia+2+EVlcL+H05Gqd2SUamCPy6T\ndn7GVmSoFaF2ViaqVbJfC2DlOhs+MVazirFcViPDWJnxHZmPGKjk0I3VS/coSFMUmT4u7DPzsomm\nD1l47DgAYOHmfd4T8ZGkU/BmPbYf7jndcRp2MSgA4FOThUVWlPfQg9eGnGY9q09u6oJRt/dwlNbL\nl58acewNh55Cxo83lum89vXx8MDIxVC1Xa4/eOXyjvYXmdCwZmU8c1E7fHBDLsbe08vWbHSDrocY\nXoeanZCFpy5oi3+d3xbXdW+Cj2/qYjpHYkUsFKGTNBlA75Z1MNTGhKhdG06ujXnETOH9cn8fvHpF\nR/znkvYR56wWhf3NYHRnZ7vWOgMt61bFUxe0xYhbu2HYlZH13Q31alQyjOWkNwO9d11n9G5VB4PU\nEZLR+9CLvjgvWFtRJ52CN8NNjJr+be2Hs2d4WFhUoZyxLc6IMhODer1JBe/bpi66Nqvt6PlGCra1\njeeNhpFtUy+S/ne5DMKdfVtEXD/4tIYRx6w4oWrFkAVWbrm+R1PkVK+EpnWybOOKVK9UodQTxIgb\nT2+GW3o1w7MXn+JrvBC/vL56ND/B9FwJM8qXy8ADNiZEMzTPFLffp5Z1q+GS007E1d0iy3XgKWVe\nUfoSaF4nC5cY1BM3pXTj6c3Qs2WdCDOjF67r0TRSFp0w57Srh89v6YYuTc3boP4dlw+YG03KKHg9\ndr2eaIZgVis6szLLTAVOzAaAzjSi/nAbbMxp+pHHQ8/YlUmwqm0klco7L7fwvGQYNEojxWxVr1j9\nLyKdKAtOGxVmVTTPX7Qjh0qq2cOTidElWRXLe6pM8Zx6c/sovWxVK3o3O8aClFTwXnA64ff9PcYz\n4x0b1QyZKPr2zp4R1+iH0OEVlojw9IXt8INJ+kboY9WYUa1SBTxx3skRxxvVrhyijuzcJEtt8BbP\nirYRjr6zp+dh9+1nNMfQs1oaDv/D0eQ8p10O7u/fGt0cjpD0XHRqA4y9p1fpxGT4B+H5we3Rsm5V\nNApb6ajnras72YakPfvkHPy9X6uQBUpjw+qInTnpjjNahJiwwhcr3dm3JYae1RLXdi/zUvr2rsj6\n6wYi4IXB7fHTvb1Djr93XWfDj6feA01/jxNzajxwWrejGZHGAk8KnohyiGia7u8KRDSWiKYT0c3+\niRc/jEwORpj5MN/fv3XI5GPz7Mjr9EPocBMNANzQs6krH2k7s4TGrb0jd7YhopCen9bo9PZpMxNN\nrOjcpJbnYXelCuXwwIA2jibhtLzUzsrEvf1aefLEGXblaWh/Yg08oFNAesV1Trt6+OX+yGBUes7r\nUB/dLUwvgGIO+0f/1qVePTWrVED7E0O9M0psQi0M6VP2/pmVxUr65f2VMyPLrlPjWmgVpb/+lV0b\no22D6qWfnx7NT0CDmpUN61Jlte30bHEC2uriu1zdTfnoxHMEaVQftCNGE+3h5kvAPpJlvHCt4Imo\nFoBPAeij9QwFMI+ZTwdwGREFNyqRCdFWIGZ2pQQ1l8oKcRgWm1FR16DtFKP28bLKYiLzomHVl81U\ny7y86rJkpxidpG32zv1WSNpzKnrwIiFExkxxY9Lygr4uaKM/rY4ZlU15k/agXVsxxvIaPTPkmMUI\nVv9hD9qqaC+xZosBXAHge92xvgAeUX//DiAXwBT9TUQ0BMAQAGjc2HyyKx7oX+CI27phy97Dhte9\ne21nvD11DRZtCZ0Zf+XyjsiuVhGFR4/jji/mA1BevJtVbDed3gz5h4pCelfx5qvbu+PHRdtQePQ4\nbuvdHGMW5IWc11fc96/Pxfgl2y2j8N19Zku8PXWt5TP7t83BpGU7LK/59q6eWJpXgH99vzTi3GtX\nnIpaHqOFPnCO0kttXLsK/vLJjc10ubsLDf/ooJPw/PgVltfUrJKJBwe0xiCDkA76XuXoO3ti2bb9\nWLl9P76Ytan0+POD2+PreVuQoy7bH3V7D5z27CRMf8SfHYaGX9cZewuPYfm2/SAi3KMzR3ZrVht3\nnNECN6smTKMectemtXFn3xa4qadyzZPnt0WXprVRKysTD/RvjfM6uAtlYYZRyN5v7+qJSct24B21\n7rod0AVs8WoItgqeiN4DoJ+e/5WZnwl7SVkANO2wF0BEwApmHg5gOADk5uYG5jPXs0UdoAUwcen2\niHMDT6mHgafUQ9NHxoUcH6xbEHJG62z8tmoXwO5edOUK5XyNae6FlnWr4b6zlcHWkaLiiPP6/DSq\nXcU2hGqWLja9meK744wWtgq+U+Na6NS4lqGCv9jGU8eqA1W9UgU8fl5b/G+OovgsbddRNnI33jO3\nn9ECxcx48eeVlteZbfSsz0XnJrXQuUktbNxTGKLgW+VUw2Pnls3F1MrKxIYXznMsox0D2pl7pmVk\nUMgeA0btJCODQlxvb9bF6B/az78Nrk+uH7lYqlPjWqhXvZJOwZu/O6cd9KDofFsFz8y3O0jnIIDK\nAAoAVFX/Thh2rkrGqza9vRLtNga7atRB++rHz4smtIWUjnp8eoATK1HZoi2LdAwKxMoHOoMotAzd\nfiCiKABDuXTpBa6uJfDZymjH2iXYCKvzRqeCElXSr+2A5gHoBeAbAB0BzPIpXUP+eU4b5DaphR8W\nbkXXZrWxbldhyPmRQ7rjuwV5FuYC80kUt4R6mnhMxCVPnt82Yrm9EW9f4zzmRukkq/5YDCZZiQgf\n39QFefmKWez5we3x9pQ16N0yMvaLxldDukeYycx4cEAbMAOnt6yDfYeK0KZeVcxZnx8mhPI/q87Y\nkxe0RfXK5bFq+0HM2bDX9Lp61Svh9jOa42+dG6Fhzcr497jlANzXhRt6NsHm/EMYMXuT/cUAvr/7\ndHw1dzNGzN4UE/fMWJJI2bzEi9JjXNZlGWqTUw13nNECl3WO3kffD/xS8J8C+ImIegNoC2C2T+ka\ncveZin2vm4kHQuucanho4Em29mA/0F43gVzZ4KOJqXOzwy3mznUZgtcKtz1MM/NHBhHObFMWxySn\neqWQGDNGdGt+gum7DqdmlUw8F7aysnMTYzdIqx58naoV8e+L2+OaD6z7KkSERweVmT4yy2fg2PES\n11EFq2SWx38uae9YwXdsVBM51SspCj4wBk+nxFfDO2lrdvXb6mxop4gCteWl588ZM/fV/d4IoD+A\n6QDOZuZIg26A8LMHUVKm4QPda7JDU8imXiF+mVACUEilLm8u1246ul69JB5hY8liJKJfvOXXalq/\nCEAViMBWJguzXhDzo+Hbjs3MvBXAKL/S84MXL+2Ah0YvcnSt/iV9Fbb45O/9WpmGC9A8GAiRPYU3\nrz4Nuw8cxVNjl5Ue+/aunhi/eJsjmZzy2LknoXFtc+8WJ2SWy8AtvZqFhKe1UwyvXXEqDh49jjpV\nM7El39gTSc9Vql+0U768tRtmrzc3j3jlgo4NMGPtHkc9rRcGd0DvFxWHMGZg2JWnYv/hItv7vDb6\n1686DfmFx/D/foicYA6nbrWKuL5HE1xpsIeuV50z4tZumLVuDwDgnWs748M/1qOFwZqOaNDLFs/e\n7uBO3s0mZZ0Cg3MB1vC+KfggMqBdDh4aHXnc6nWc2SY7whzwDwcb8Bq95PM7KMpSr+A1DxE/GdLH\n2SItK4jI0qvHqA7bebSEo2375pTTW9bB6Ra2ea9UqlAOr17hbMVso9pV8OT5bfHMj8o7dLoQy2ub\n1z6wThQ8EeEZE/OW1+f3bFkHPdUyb1m3qut35gStrZxUT7FXxwurWEROiyvZzGGJX5kSQ+Lh1VK6\nItXb7cFGP8mamjlMWSj05QWKslWhCRUjlCi8aIJMSvfgq1Uqjws7NsAPC7fG7BlPX9QOz41b7jji\nY7wI73k90L81iICXJq5ynIaZR40Vb1/TCXM35OOW3s3w7fw8+xuSADeN+/NbumLknE1RB+56dNBJ\nUcU+D7JC0rsWBwXnHRhjmS/tdKJhlMxEk9IKPiOD8PpVp8VUwbfIroqPfNy+zC/Ch6ND+7XCvkPH\nXCl4PU6r/7nt65d67wy78lT8/X9/eXpeEHGyFaQbjx8rbo/SdBFoBR9vLxpf0rBO5eUo9jaIJSlt\nojEjOP2G+OK2YYUGHguwxogxyZjzQC900nrwAWqITssoSDI7IaUU/H8v62C46cQLg9tbxlABgG7N\nTkDHE2vg4Shn9c9plxPVpr7R8s41nTCgbUSkCAAAuXzbUSzMjODuM+M3mSYET6nrsXLvTBR2xXVu\n+3po16C646izQSGlTDR/y22EvxlsfHxl18ZoWbcqLnt3pum9WRXLm8Z6d8N71+XaXxRDBrWvbxiQ\nCohOSUejMM7vUB//PCc4iz+8EiSFZIefH2e/0UYXTkxeQaFmlUyMC4ttnwykVA/eCr2CSqJ65Stu\nF9+EhipwryasQqwmE6X5SKKMBNmkFkTRglxe0ZBSPXhrEvcC/3lOGyzftj9hz9dwHQY1yuf1aVUH\nDWpUwl1JNqwNJxnbfkBiXRmSjCaaZCWNFHxZdYp3g9Vi5ySaeHsv1KySiRmP9ovrM2NJkNz67Aid\nZA2W+krUmopkGoH5RdqYaAQPPfiAKYZEkYyl4HZCPZ6UVqs0VLjxJsDVIHak45ccSE5Tg+CNIL9q\n0e/xIy0VfLpWLbeTrMnk5RAPkqk4QtYwJFAOI+Ld0XDyvCR6ta5IUwWfnrhtV6la6V2ThEOfIE+y\nIgndJJMVUfBphNse/PU9msRIEiHWBDk4XLy/l3f1VZwc2uTY74KWaqSNF42+s5CuHQe3DctsJ6R0\nIxltxrHYbtEv4l2eZ55U13aD8VQdTaRlDz41X6U94hXjjSDGTrEjyK9a6mH8SBsFr2+bDWpWTpgc\nycatvZohK7NcosVIKKe3UDbAiGZHoHgTjy0DvRLIePApStqYaDRym9RC1Yppl23PPHF+WzxhsdNT\nOtC0TpbtED9o6NV70JR9IOPBB6yM/CJtevCCkE4ETanrCeIEsNjgk5wUfX+CYEiQJ1k1gtQmq1ZK\nzVG9KwVPRDWIaDwRTSSiMUSUqR7/kIhmEtETsRHTP4Ja2QXBT0IXOgWr0gexDVYsn5rzTG578NcA\neIWZBwDYDmAgEQ0GUI6ZewBoTkSt/BbSD1J1CCYIdgRRoQLB6sGnKq4UPDO/zcyT1D+zAewE0BfA\nKPXYRACGu2YQ0RAimktEc3ft2uVR3OgJWm9GEGKN1Pj0xVLBE9F7RDRV9+9J9XgPALWYeRaALAB5\n6i17ARjuF8fMw5k5l5lzs7OzfcyCIAhWBHnCVYgtljMLzHx7+DEiqg3gDQCXqocOAtAcy6sioBO3\nMhoU0pWg6feyhWPSKmON20nWTABfA3iUmTeqh+ehzCzTEcAG36SLBQGr7IIQa4Lm4x00eVIZt75B\ntwDoBOBxInocwDsAvgMwjYgaABgEoLu/IvqDdBYEIVhIk4w9rhQ8M78DRamHQER9AfQH8CIzF/gj\nWmyQvoMgJBYJVRA/fPHuZ+Z8lHnSBJJGtZVpggs6NkiwJIKQ3gQxVEGqkprLtww4sVYVLPhXf9Ss\nUiHRoghCWiOuyvEjbRQ8ANTKyky0CIIgqIiJJvYE0qVREITUpcxEI8QaUfCCIMQVMdDED1HwgiAk\nBDHRxB5R8IIgxJfSLnywNHyVFNy5LK0mWQVBCAClej1YxprZj/VDUXGwPjrRIgpeEIS4oqnQoEUs\nqFYp9VyoxUQjCEJcqayaQnq1rJNgSVIf6cELghBXqleqgCkP9kWDmpUSLUrKIwpeEIS406xOVqJF\nSAvERCMIgpCiiIIXBEFIUUTBC4IgpCii4AVBEFIUUfCCIAgpiih4QRCEFEUUvCAIQooiCl4QBCFF\nEQUvCIKQooiCFwRBSFFcK3giqk1E/YlIIgUJgiAEGFcKnohqAfgRQFcAU4goWz3+IRHNJKInYiCj\nIAiC4AG3wcY6ALifmWepyr4TEWUBKMfMPYjoIyJqxcyr/RdVEARBcIOrHjwz/6Yq9z5QevEzAfQF\nMEq9ZCKAXkb3EtEQIppLRHN37doVhciCIAiCEywVPBG9R0RTdf+eJCICcAWAfABFALIA5Km37AWQ\nY5QWMw9n5lxmzs3OzvYxC4IgCIIRliYaZr7d5NTdRPQsgAsBHARQWT1eFeKZIwiCEAjcTrI+TETX\nq3/WBLAPwDyUmWU6Atjgm3SCIAiCZ9xOsg4HMIqIbgWwBIrNvRqAaUTUAMAgAN39FVEQBEHwgisF\nz8z5APqHHd5PRH3V4y8yc4FPsgmCIAhR4MuerKriH2V7oSAIghA3ZEJUEAQhRREFLwiCkKL4YqIR\nkocPb8jFwaPHEy2GIAhxQBR8mtHvZMN1aIIgpCBiohEEQUhRpAcvCCnKZzd3RcHhokSLISQQUfCC\nkKL0aS0xn9IdMdEIgiCkKKLgBUEQUhRR8IIgCCmKKHhBEIQURRS8IAhCiiIKXhAEIUURBS8IgpCi\niIIXBEFIUYiZ4/9Qol0ANnq8vQ6A3T6Kk2ykc/7TOe9Aeudf8q7QhJkdr2BLiIKPBiKay8y5iZYj\nUaRz/tM570B651/y7i3vYqIRBEFIUUTBC4IgpCjJqOCHJ1qABJPO+U/nvAPpnX/JuweSzgYvCIIg\nOCMZe/CCIAiCA0TBC4IgpChJpeCJ6EMimklETyRalnhAROWJaBMRTVX/tSeip4noTyJ6K9HyxRIi\nyiGiaervCkQ0loimE9HNZsdShbC8NySiLbo6kK0eT7m2QEQ1iGg8EU0kojFElGmUzzTKe0jbV69z\n1f6TRsET0WAA5Zi5B4DmRNQq0TLFgQ4ARjJzX2buCyATQC8AXQHsJKKzEylcrCCiWgA+BZClHhoK\nYB4znw7gMiKqZnIs6THIezcAz2l1gJl3pXBbuAbAK8w8AMB2AFciLJ9plPdHoGv7zLyYiDrDZftP\nGgUPoC+AUerviVAymup0B3A+Ec0hog8B9AMwmpWZ8QkAeidUuthRDOAKAPvVv/ui7N3/DiDX5Fgq\nEJ737gBuJaL5RPQf9VhfpGBbYOa3mXmS+mc2gGsRmc++BseSHoO8H4eu7RNReQBnwGX7TyYFnwUg\nT/29F0BOAmWJF38COJuZuwKoAKAy0qAMmHk/MxfoDhm9+5SsDwZ5Hw9FqXUB0IOIOiBF865BRD0A\n1AKwGWny3jV0eZ+E0LZ/LjzkPZkU/EEoCg4AqiK5ZPfKImbepv6ei/QsA8A43+lSFjOY+QAzFwNY\nAKAVUjjvRFQbwBsAbkaavfewvIe3fU/vPZkKZx7KhmMdAWxInChx43Mi6khE5QBcDOULnm5lABi/\n+3SpDxOIqD4RVQEwAMASpGjeiSgTwNcAHmXmjUij926Q9/C2vxAe8l4+NuLGhO8ATCOiBgAGQbFN\npjrPABgBgAD8AODfUMpgGICB6r904FMAPxFRbwBtAcyGMlQNP5aKPA1gCoBjAN5l5pVEtA2p2RZu\nAdAJwONE9DiAjwFcF5ZPRnrkfQqAz6G2fWb+hYgyADzvpv0n1UpW1cOgP4DfmXl7ouVJBERUGcB5\nAOYz87pEyxMv1AbdC8AEzUZtdCxdSJe2YJTPdMm7EW7bf1IpeEEQBME5yWSDFwRBEFwgCl4QBCFF\nEQUvCIKQooiCFwRBSFFEwQuCIKQo/x+GZWRD1eZaUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16eb5048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 滤波后的RR间期信号rr_filt即为后面要建立的HMM的观察量，为了减少观察量的取值数目，查找（-0.3,0.3）s区间外的数组，置为-0.3/0.3，然后除以8取整\n",
    "# 因此，RR间期共有[-37,37]共75种可能的取值（由RMSSD公式，HMM观察值共有[0,74]共75种取值）\n",
    "# np.where(rr_filt>0.3) or np.where(rr_filt<-0.3)\n",
    "hmm_obsv_rr = []\n",
    "for value in rr_filt:\n",
    "    if(value < -0.3):\n",
    "        value = -37 #int(-0.3*1000/8)\n",
    "    elif(value > 0.3):\n",
    "        value = 37 #int(0.3*1000/8)\n",
    "    else:\n",
    "        value = int(1000*value/8)\n",
    "    hmm_obsv_rr.append(value)\n",
    "\n",
    "plt.plot(axis_rr_new,hmm_obsv_rr)\n",
    "plt.title(\"The observation list of HMM: Time(30s)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PYZHmenA//zgLNPe1fg75Rr58CPhCt2nHep/rCdoOST6R5CPdzTcBP2RBLonQfVj6ZeBT\nVfVKd/dC9L6FRep9kXodZmH6H7KfL1Lvg/nyZ8yg9133BaXuQ6ULwM8A3wOeA94AXAH+nu6SCPP4\n7dkkHwX+FLje3fUC8HUWoPcNSS5X1Uo3fgvwt8A3gV9hvfefzLC9LSX5WRZonjdszPeCzfXgfv5X\nrK/a537uh+TLp1j/TGNHe991wb6V7g9yHHixqm7Nup9xLHjvj7G+ovnGvL5YNyzyPMNizfWgRZ77\nWfRusEtSY3bdMXZJap3BLkmNMdglqTEGuyQ1xmCXpMb8P8JsxH6K7ovXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x171e42e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dexvtnFr+3KZrXrF9t83Zhtf9DjZddxvtzLoNK27L1Pvj7FerV2xOjnTfLenpiLjU2gtt\nUB/aKHW/nWXa14XfQRfaUKQPbZzGZfcAkFgXD2wAQG8Q4gCQGCEOAIkR4gCQGCEOAIn9HwMbr89M\nOBbPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16ec1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KstestResult(statistic=0.3162776458295986, pvalue=0.0)\n",
      "(0.9565344452857971, 0.0)\n",
      "KstestResult(statistic=0.44912381413452807, pvalue=0.0)\n",
      "(0.9743260145187378, 1.5414283107572988e-44)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import kstest\n",
    "from scipy import stats\n",
    "\n",
    "plt.hist(hmm_obsv_rr, bins=256, normed=1,edgecolor='None',facecolor='red')  #画出hmm_obsv_rr的直方图\n",
    "plt.show()\n",
    "plt.hist(rr_filt, bins=256, normed=1,edgecolor='None',facecolor='red')  #画出hmm_obsv_rr的直方图\n",
    "plt.show()\n",
    "test_stat1 = kstest(hmm_obsv_rr, 'norm') #p-value=0<0.05,拒绝假设，假设不成立（hmm_obsv_rr不服从正态分布）\n",
    "print(test_stat1)\n",
    "print(stats.shapiro(hmm_obsv_rr))#第二个为p值，<0.5,不服从正态分布\n",
    "test_stat2 = kstest(rr_filt, 'norm') #p-value=0<0.05,拒绝假设，假设不成立（rr_filt不服从正态分布）\n",
    "print(test_stat2)\n",
    "print(stats.shapiro(rr_filt))#第二个为p值，<0.5,不服从正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "# 为消除不同个体差异应用直方图匹配法：先寻找hmm_obsv_rr的90%有效区间[-a,a]\n",
    "for a in range(37):\n",
    "    count_a = 0 # 将介于区间[-a,a]的r计数，初始化为0\n",
    "    for r in hmm_obsv_rr:\n",
    "        if(r > -a and r < a):\n",
    "            count_a += 1\n",
    "    if(count_a > 0.9*len(hmm_obsv_rr)):\n",
    "        choose_a = count_a\n",
    "        break\n",
    "print(a)# 即90%的点落在[-8,8]之间\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---  There is this folder!  ---\n"
     ]
    }
   ],
   "source": [
    "## 预处理模块：将不同睡眠分期状态及对应的RIS观察值写进文件hmm_obsv.txt\n",
    "cwd = os.getcwd()\n",
    "HMM_data_path = os.path.join(cwd, 'HMM_data')\n",
    "# HMM_data_path = cwd +\"\\\\HMM_data\"\n",
    "folder = os.path.exists(HMM_data_path)  \n",
    "if not folder:                   #判断是否存在文件夹如果不存在则创建为文件夹  \n",
    "    os.makedirs(HMM_data_path)            #makedirs 创建文件时如果路径不存在会创建这个路径  \n",
    "    print (\"---  New folder...  ---\")  \n",
    "    print (\"---  OK  ---\" ) \n",
    "else:  \n",
    "    print (\"---  There is this folder!  ---\" ) \n",
    "\n",
    "file =  os.path.join(HMM_data_path, 'hmm_obsv.txt') \n",
    "# file = HMM_data_path +\"\\\\hmm_obsv.txt\"\n",
    "# fid = open(file,\"w\")\n",
    "# fid.write(\"Hello, world\\n\")\n",
    "\n",
    "# fid.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import signal\n",
    "# slp01a 共有len(st)=240个睡眠分期，因为选取的RIS序列为包含该睡眠分期的T=8min片段，因此可训练睡眠分期共有240-15=225个，即st[15:len(st)]\n",
    "# 观察值序列分别为[0,8min],[0.5,8.5min],[1,9min]...共225个片段对应的经过插值滤波后的8分钟RR间期，下面给出实现代码\n",
    "\n",
    "T = 8\n",
    "fs = record.fs\n",
    "min_sample = 60*fs\n",
    "\n",
    "# fid = open(file,\"w\")\n",
    "data_for_train = []\n",
    "for st_num in range(len(st)-2*T+1):\n",
    "    sig_start = 0.5*st_num*min_sample\n",
    "    sig_end = (T+0.5*st_num)*min_sample\n",
    "    hmm_rr_st_index = []\n",
    "    for index,value in enumerate(ann_ecg.sample):\n",
    "        if(value >= sig_start and value <= sig_end):\n",
    "            hmm_rr_st_index.append(index)\n",
    "    hmm_rr_st = ann_ecg.sample[hmm_rr_st_index[0]:hmm_rr_st_index[-1]] # 每个睡眠分期对应的T=8分钟RR间期索引序列\n",
    "\n",
    "    # 获取RR间期序列，并重采样\n",
    "    st_rr,st_R_pot = RR(hmm_rr_st,fs)\n",
    "\n",
    "    st_R_pot = st_R_pot-st_R_pot[0]\n",
    "    hmm_rr_st = Interplt(st_rr,st_R_pot)[1] #三次样条插值函数Interplt\n",
    "\n",
    "    hmm_rr_st_num = hmm_rr_st - signal.medfilt(hmm_rr_st,101) #将滤波后的RR间期序列赋给对应睡眠分期的观测值\n",
    "    hmm_obsv_rr = []\n",
    "    #限幅和编码\n",
    "    for value in hmm_rr_st_num:\n",
    "        if(value < -0.3):\n",
    "            value = -37 #int(-0.3*1000/8)\n",
    "        elif(value > 0.3):\n",
    "            value = 37 #int(0.3*1000/8)\n",
    "        else:\n",
    "            value = int(1000*value/8)\n",
    "        hmm_obsv_rr.append(value)\n",
    "#     # 由RMSSD公式计算T=8分钟RR间期对应的HRV\n",
    "#     hmm_obsv_hrv = 0\n",
    "#     for index in range(0,len(hmm_obsv_rr)-1):\n",
    "#         hmm_obsv_hrv+=math.pow((hmm_obsv_rr[index+1]-hmm_obsv_rr[index]),2)\n",
    "#     hmm_obsv_hrv = math.sqrt(hmm_obsv_hrv/(len(hmm_obsv_rr)-1))\n",
    "        \n",
    "    data_for_train.append([st[st_num+2*T-1],hmm_obsv_rr]) #将睡眠标签及对应8分钟观察值存进数组data_for_train\n",
    "#     print(st[st_num+2*T-1],len(hmm_rr_st_num))\n",
    "#     fid.write(st[st_num+2*T-1]+\" \"+str(hmm_rr_st_num)+\"\\n\")\n",
    "    \n",
    "# fid.close() \n",
    "        \n",
    "# np.savetxt(file, np.array(str(data_for_train)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225\n",
      "2\n",
      "[0, 2, 4, 7, 6, 4, 4, 3, 3, 2]\n"
     ]
    }
   ],
   "source": [
    "fid = open(file,\"w\")\n",
    "# fid.write(str(data_for_train))\n",
    "for line in data_for_train:\n",
    "    fid.write(line[0]+\",\")\n",
    "    for content in line[1]:\n",
    "        fid.write(str(content)+\",\")\n",
    "    fid.write(\"\\n\")\n",
    "fid.close()\n",
    "print(len(data_for_train))\n",
    "print(data_for_train[0][0])\n",
    "print(data_for_train[0][1][:10])\n",
    "# for i in range(len(data_for_train)):\n",
    "#     print(data_for_train[i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  2.  4. ... 10. 18. 31.]\n",
      " [ 1.  0.  0. ...  9.  3.  0.]\n",
      " [17. 12. 12. ...  0.  0.  0.]\n",
      " ...\n",
      " [ 2.  0.  0. ... 34. 37. 37.]\n",
      " [ 5.  5.  4. ...  4.  2.  0.]\n",
      " [ 9.  9.  7. ...  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "## 读取睡眠标签及观察值序列文件hmm_obsv.txt # np.load(file)#调用不成功\n",
    "file =  os.path.join(HMM_data_path, 'hmm_obsv.txt') \n",
    "fid = open(file,\"r\")\n",
    "note = fid.readlines()\n",
    "fid.close()\n",
    "\n",
    "states = [] # 存储hmm_obsv.txt的睡眠标签\n",
    "\n",
    "T = 960 #设置观察序列长度T=2*60*8=960\n",
    "observations_data = np.empty((len(note),T)) #用来存储观察值序列的矩阵，每行为一个观察值序列，每个序列长度固定为T=960\n",
    "Ti = 0\n",
    "\n",
    "for line in note:\n",
    "    line_rr = line.split(\",\")\n",
    "    states.append(line_rr[0])#将睡眠标签写进states数组\n",
    "    line_new = [] # 对应RR间期\n",
    "    for content in line_rr[1:-1]:  #去掉第一位标签，最后一位换行符\n",
    "        line_new.append(int(content)) #int 的作用是将str转为int\n",
    "\n",
    "#     print(line_rr[0])\n",
    "#     print(line_new)\n",
    "#     print(\"==================\")\n",
    "\n",
    "    # 为了保证观察序列长度一致，设置观察序列长度T=2*60*8=960，因此从序列最后开始倒数选取T个观察值\n",
    "    #（why倒数选取T个观察值：因为越后面的RR间期越能反应当前的睡眠分期）\n",
    "    line_new = line_new[-T:]\n",
    "    observations_data[Ti:] = line_new\n",
    "    Ti+=1\n",
    "\n",
    "print(observations_data)\n",
    "\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import hmmlearn\n",
    "\n",
    "# 预处理自此结束，下面调用python库中的hmmlearn进行模型训练及预测，见sleep_hrv_model.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "将75种观察值及对应的位置转换为字典:\n",
      " {-37: 0, -36: 1, -35: 2, -34: 3, -33: 4, -32: 5, -31: 6, -30: 7, -29: 8, -28: 9, -27: 10, -26: 11, -25: 12, -24: 13, -23: 14, -22: 15, -21: 16, -20: 17, -19: 18, -18: 19, -17: 20, -16: 21, -15: 22, -14: 23, -13: 24, -12: 25, -11: 26, -10: 27, -9: 28, -8: 29, -7: 30, -6: 31, -5: 32, -4: 33, -3: 34, -2: 35, -1: 36, 0: 37, 1: 38, 2: 39, 3: 40, 4: 41, 5: 42, 6: 43, 7: 44, 8: 45, 9: 46, 10: 47, 11: 48, 12: 49, 13: 50, 14: 51, 15: 52, 16: 53, 17: 54, 18: 55, 19: 56, 20: 57, 21: 58, 22: 59, 23: 60, 24: 61, 25: 62, 26: 63, 27: 64, 28: 65, 29: 66, 30: 67, 31: 68, 32: 69, 33: 70, 34: 71, 35: 72, 36: 73, 37: 74}\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-155-c05ae06ec817>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     40\u001b[0m     \u001b[0mobservations_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#int 的作用是将str转为int\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[0mobs_seq_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_observations_to_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservations_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mobservations_label_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m \u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mB\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaum_welch_train\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobs_seq_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     43\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"hello\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-132-1a87d70e84c9>\u001b[0m in \u001b[0;36mbaum_welch_train\u001b[1;34m(self, observations, criterion)\u001b[0m\n\u001b[0;32m     93\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mlev\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_levels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m                     \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobservations\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlev\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m                     \u001b[0mnewB\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlev\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgamma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0msumgamma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     96\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mnewpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mcriterion\u001b[0m \u001b[1;32mand\u001b[0m                                 \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mA\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mnewA\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mcriterion\u001b[0m \u001b[1;32mand\u001b[0m                                 \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mB\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mnewB\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# seterr\n",
    "from numpy import seterr\n",
    "np.seterr(divide='ignore',invalid='ignore')\n",
    "\n",
    "## 给定6种睡眠分期对应的观察序列，分别利用Baum-Welch算法学习6个模型参数λi=(Ai,Bi,πi)\n",
    "# 轮训1次\n",
    "# 初始化\n",
    "A = np.array([[0.20, 0.16, 0.16, 0.16, 0.16, 0.16],\n",
    "              [0.16, 0.20, 0.16, 0.16, 0.16, 0.16],\n",
    "              [0.16, 0.16, 0.20, 0.16, 0.16, 0.16],\n",
    "              [0.16, 0.16, 0.16, 0.20, 0.16, 0.16],\n",
    "              [0.16, 0.16, 0.16, 0.16, 0.20, 0.16],\n",
    "              [0.16, 0.16, 0.16, 0.16, 0.16, 0.20]])\n",
    "\n",
    "B_random = np.random.random((6,75))\n",
    "B = np.empty((6,75))\n",
    "line_i = 0\n",
    "for line in B_random:\n",
    "    line = line/sum(line)\n",
    "    B[line_i] = line\n",
    "    line_i+=1\n",
    "    \n",
    "pi = np.array([0.16, 0.16, 0.16, 0.16, 0.20, 0.16])\n",
    "model = HMM(A,B,pi)\n",
    "\n",
    "observations = list(range(-37,38))\n",
    "observations_label_index = gengerate_index_map(observations)\n",
    "print(\"将75种观察值及对应的位置转换为字典:\\n\",observations_label_index)\n",
    "\n",
    "# for line in note:\n",
    "#     line_rr = line.split(\",\")\n",
    "#     observations_data = [] # 对应RR间期(观察值序列)\n",
    "#     for content in line_rr[1:-1]:  #去掉第一位标签，最后一位换行符\n",
    "#         observations_data.append(int(content)) #int 的作用是将str转为int\n",
    "#     obs_seq_index = convert_observations_to_index(observations_data,observations_label_index)\n",
    "#     A,B,pi = model.baum_welch_train(obs_seq_index)\n",
    "\n",
    "observations_data = []\n",
    "for content in note[0].split(\",\")[1:-1]: \n",
    "    observations_data.append(int(content)) #int 的作用是将str转为int\n",
    "obs_seq_index = convert_observations_to_index(observations_data,observations_label_index)\n",
    "A,B,pi = model.baum_welch_train(np.array(obs_seq_index)[:300])\n",
    "print(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "## 监督学习方法（因为8分钟每个RR间期对应的睡眠状态不明确，下面将8分钟都默认为一个睡眠状态，误差大，不可取，考虑非监督学习算法Baum-Welch）\n",
    "# 计算初始概率start_probability\n",
    "stations = ['1','2','3','4','R','W']\n",
    "count_stations = np.zeros(6)\n",
    "for index in range(6):\n",
    "    count_stations[index]=states.count(stations[index])\n",
    "\n",
    "print(\"['1','2','3','4','R','W']对应频数：\",count_stations)\n",
    "print(\"Number of count_stations: %d\" % sum(count_stations))\n",
    "start_probability = count_stations/sum(count_stations)\n",
    "print(\"Start_probability:\\n\",start_probability)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "# 转移概率矩阵transition_probability\n",
    "transition_tuple = [] #转移状态数组\n",
    "for index,value in enumerate(states[1:]):\n",
    "    transition_tuple.append([states[index-1],value])\n",
    "# transition_tuple.count(['2', '2'])\n",
    "transition_count = np.zeros((6,6))\n",
    "transition_probability = np.zeros((6,6)) \n",
    "for line in range(6):\n",
    "    for colum in range(6):\n",
    "        transition_count[line][colum] = transition_tuple.count([stations[line], stations[colum]])\n",
    "    transition_probability[line] = transition_count[line]/sum(transition_count[line])\n",
    "# print(\"Transition_count matric:\\n\",transition_count)\n",
    "print(\"Transition_probability matric :\\n\",transition_probability)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "# 观测（发射）概率矩阵emission_probability\n",
    "observations = list(range(-37,38)) #观测值75个\n",
    "emission_tuple = [] # 状态发射到观测值的存储数组\n",
    "\n",
    "for line in note:\n",
    "    line_rr = line.split(\",\")\n",
    "    line_st = line_rr[0] # 每一行对应睡眠状态\n",
    "    line_new = [] # 对应RR间期\n",
    "    for content in line_rr[1:-1]:  #去掉第一位标签，最后一位换行符\n",
    "        line_new.append(int(content))\n",
    "        emission_tuple.append([line_st,content])\n",
    "\n",
    "emission_count = np.zeros((6,75))\n",
    "emission_probability = np.zeros((6,75))\n",
    "for line in range(6):\n",
    "    for colum in range(75):\n",
    "#         print([stations[line],str(observations[colum])])\n",
    "        emission_count[line][colum] = emission_tuple.count([stations[line],str(observations[colum])]) #str作用为将int观测值转换为str类型\n",
    "\n",
    "    emission_probability[line] = emission_count[line]/sum(emission_count[line])\n",
    "# print(\"Emission_count matric :\\n\",emission_count)\n",
    "# print(\"Emission_probability matric :\\n\",emission_probability)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 至此，HMM模型已搭建完毕\n",
    "# 下面转换为HMM的概率计算问题：给定6个模型λi=(Ai,Bi,πi)和观测序列O={o1,o2,...,oT}，计算在模型λi下观测序列O出现的概率P(O|λi)\n",
    "# 求index(max(P(O|λi))：概率最大时的模型所对应的睡眠分期，即为此观测值对应的睡眠状态\n",
    "pi = start_probability\n",
    "A = transition_probability\n",
    "B = emission_probability\n",
    "h = HMM(A,B,pi)\n",
    "# 75种状态序列observations = list(range(-37,38))\n",
    "obs_seq =  [1, 2, 4, 5, 7, 5, 4, 4, 4, 2]\n",
    "obs_seq_index = convert_observations_to_index(obs_seq,observations_label_index)\n",
    "# print(\"观察序列对应的数组:\\n\",obs_seq_index)\n",
    "\n",
    "# 计算P(o|lambda)\n",
    "F = h._forward(obs_seq_index)\n",
    "print (\"forward: P(O|lambda) = %f\" %sum(F[:,-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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